In addition to the validation split of the RSNA dataset, we evaluate our resulting models on two external test sets—the CQ500 dataset (436 examinations with a positive rate of 49%) [1]} and the CT-ICH dataset (75 examinations with a positive rate of 48%) [2]}, [3]}, [4]}.The CQ500 dataset is available at: http://headctstudy.qure.ai/dataset; the CT-ICH dataset is available at: https://physionet.org/content/ct-ich/1.3.1/. Table REF  shows the distribution of positive and negative examinations in the two external test sets and their total number of images. We note that the CQ500 dataset only provides examination-level labels, while the CT-ICH dataset provides both image-level labels and manual pixel-level segmentations of the bleeds performed by two expert radiologists. Hence, we extend the CQ500 dataset with the ICH bounding box annotations provided for this dataset by three radiologists with varying degree of experience, available in the BHX dataset [5]}, [4]}.The BHX dataset is available at: https://physionet.org/content/bhx-brain-bounding-box/1.1/. We include details on the preprocessing of the images for all three datasets in sec:datapreprocessing.
